Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Tuesday 11 August 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 204,109 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 204,099 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
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## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
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## 80 2020-05-19 East of England 15
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## 135 2020-07-13 East of England 7
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## 144 2020-07-22 East of England 1
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 1
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
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## 539 2020-04-22 North East and Yorkshire 84
## 540 2020-04-23 North East and Yorkshire 63
## 541 2020-04-24 North East and Yorkshire 72
## 542 2020-04-25 North East and Yorkshire 69
## 543 2020-04-26 North East and Yorkshire 65
## 544 2020-04-27 North East and Yorkshire 65
## 545 2020-04-28 North East and Yorkshire 57
## 546 2020-04-29 North East and Yorkshire 69
## 547 2020-04-30 North East and Yorkshire 57
## 548 2020-05-01 North East and Yorkshire 64
## 549 2020-05-02 North East and Yorkshire 48
## 550 2020-05-03 North East and Yorkshire 40
## 551 2020-05-04 North East and Yorkshire 49
## 552 2020-05-05 North East and Yorkshire 40
## 553 2020-05-06 North East and Yorkshire 51
## 554 2020-05-07 North East and Yorkshire 45
## 555 2020-05-08 North East and Yorkshire 42
## 556 2020-05-09 North East and Yorkshire 44
## 557 2020-05-10 North East and Yorkshire 40
## 558 2020-05-11 North East and Yorkshire 29
## 559 2020-05-12 North East and Yorkshire 27
## 560 2020-05-13 North East and Yorkshire 28
## 561 2020-05-14 North East and Yorkshire 31
## 562 2020-05-15 North East and Yorkshire 32
## 563 2020-05-16 North East and Yorkshire 35
## 564 2020-05-17 North East and Yorkshire 26
## 565 2020-05-18 North East and Yorkshire 30
## 566 2020-05-19 North East and Yorkshire 27
## 567 2020-05-20 North East and Yorkshire 22
## 568 2020-05-21 North East and Yorkshire 33
## 569 2020-05-22 North East and Yorkshire 22
## 570 2020-05-23 North East and Yorkshire 18
## 571 2020-05-24 North East and Yorkshire 26
## 572 2020-05-25 North East and Yorkshire 21
## 573 2020-05-26 North East and Yorkshire 21
## 574 2020-05-27 North East and Yorkshire 22
## 575 2020-05-28 North East and Yorkshire 21
## 576 2020-05-29 North East and Yorkshire 25
## 577 2020-05-30 North East and Yorkshire 20
## 578 2020-05-31 North East and Yorkshire 20
## 579 2020-06-01 North East and Yorkshire 17
## 580 2020-06-02 North East and Yorkshire 23
## 581 2020-06-03 North East and Yorkshire 23
## 582 2020-06-04 North East and Yorkshire 17
## 583 2020-06-05 North East and Yorkshire 18
## 584 2020-06-06 North East and Yorkshire 21
## 585 2020-06-07 North East and Yorkshire 14
## 586 2020-06-08 North East and Yorkshire 11
## 587 2020-06-09 North East and Yorkshire 12
## 588 2020-06-10 North East and Yorkshire 19
## 589 2020-06-11 North East and Yorkshire 7
## 590 2020-06-12 North East and Yorkshire 9
## 591 2020-06-13 North East and Yorkshire 10
## 592 2020-06-14 North East and Yorkshire 11
## 593 2020-06-15 North East and Yorkshire 9
## 594 2020-06-16 North East and Yorkshire 10
## 595 2020-06-17 North East and Yorkshire 9
## 596 2020-06-18 North East and Yorkshire 11
## 597 2020-06-19 North East and Yorkshire 6
## 598 2020-06-20 North East and Yorkshire 5
## 599 2020-06-21 North East and Yorkshire 4
## 600 2020-06-22 North East and Yorkshire 7
## 601 2020-06-23 North East and Yorkshire 8
## 602 2020-06-24 North East and Yorkshire 10
## 603 2020-06-25 North East and Yorkshire 4
## 604 2020-06-26 North East and Yorkshire 7
## 605 2020-06-27 North East and Yorkshire 4
## 606 2020-06-28 North East and Yorkshire 5
## 607 2020-06-29 North East and Yorkshire 2
## 608 2020-06-30 North East and Yorkshire 7
## 609 2020-07-01 North East and Yorkshire 1
## 610 2020-07-02 North East and Yorkshire 4
## 611 2020-07-03 North East and Yorkshire 4
## 612 2020-07-04 North East and Yorkshire 4
## 613 2020-07-05 North East and Yorkshire 3
## 614 2020-07-06 North East and Yorkshire 2
## 615 2020-07-07 North East and Yorkshire 3
## 616 2020-07-08 North East and Yorkshire 3
## 617 2020-07-09 North East and Yorkshire 0
## 618 2020-07-10 North East and Yorkshire 3
## 619 2020-07-11 North East and Yorkshire 1
## 620 2020-07-12 North East and Yorkshire 4
## 621 2020-07-13 North East and Yorkshire 1
## 622 2020-07-14 North East and Yorkshire 1
## 623 2020-07-15 North East and Yorkshire 2
## 624 2020-07-16 North East and Yorkshire 3
## 625 2020-07-17 North East and Yorkshire 1
## 626 2020-07-18 North East and Yorkshire 2
## 627 2020-07-19 North East and Yorkshire 2
## 628 2020-07-20 North East and Yorkshire 1
## 629 2020-07-21 North East and Yorkshire 1
## 630 2020-07-22 North East and Yorkshire 6
## 631 2020-07-23 North East and Yorkshire 0
## 632 2020-07-24 North East and Yorkshire 1
## 633 2020-07-25 North East and Yorkshire 5
## 634 2020-07-26 North East and Yorkshire 1
## 635 2020-07-27 North East and Yorkshire 0
## 636 2020-07-28 North East and Yorkshire 2
## 637 2020-07-29 North East and Yorkshire 1
## 638 2020-07-30 North East and Yorkshire 0
## 639 2020-07-31 North East and Yorkshire 1
## 640 2020-08-01 North East and Yorkshire 3
## 641 2020-08-02 North East and Yorkshire 2
## 642 2020-08-03 North East and Yorkshire 1
## 643 2020-08-04 North East and Yorkshire 1
## 644 2020-08-05 North East and Yorkshire 1
## 645 2020-08-06 North East and Yorkshire 4
## 646 2020-08-07 North East and Yorkshire 0
## 647 2020-08-08 North East and Yorkshire 1
## 648 2020-08-09 North East and Yorkshire 2
## 649 2020-03-01 North West 0
## 650 2020-03-02 North West 0
## 651 2020-03-03 North West 0
## 652 2020-03-04 North West 0
## 653 2020-03-05 North West 1
## 654 2020-03-06 North West 0
## 655 2020-03-07 North West 0
## 656 2020-03-08 North West 1
## 657 2020-03-09 North West 0
## 658 2020-03-10 North West 0
## 659 2020-03-11 North West 0
## 660 2020-03-12 North West 2
## 661 2020-03-13 North West 3
## 662 2020-03-14 North West 1
## 663 2020-03-15 North West 4
## 664 2020-03-16 North West 2
## 665 2020-03-17 North West 4
## 666 2020-03-18 North West 6
## 667 2020-03-19 North West 7
## 668 2020-03-20 North West 10
## 669 2020-03-21 North West 11
## 670 2020-03-22 North West 13
## 671 2020-03-23 North West 15
## 672 2020-03-24 North West 21
## 673 2020-03-25 North West 21
## 674 2020-03-26 North West 29
## 675 2020-03-27 North West 36
## 676 2020-03-28 North West 28
## 677 2020-03-29 North West 46
## 678 2020-03-30 North West 67
## 679 2020-03-31 North West 52
## 680 2020-04-01 North West 86
## 681 2020-04-02 North West 96
## 682 2020-04-03 North West 95
## 683 2020-04-04 North West 98
## 684 2020-04-05 North West 102
## 685 2020-04-06 North West 100
## 686 2020-04-07 North West 135
## 687 2020-04-08 North West 127
## 688 2020-04-09 North West 119
## 689 2020-04-10 North West 117
## 690 2020-04-11 North West 138
## 691 2020-04-12 North West 125
## 692 2020-04-13 North West 129
## 693 2020-04-14 North West 131
## 694 2020-04-15 North West 114
## 695 2020-04-16 North West 135
## 696 2020-04-17 North West 98
## 697 2020-04-18 North West 113
## 698 2020-04-19 North West 71
## 699 2020-04-20 North West 83
## 700 2020-04-21 North West 76
## 701 2020-04-22 North West 86
## 702 2020-04-23 North West 85
## 703 2020-04-24 North West 66
## 704 2020-04-25 North West 66
## 705 2020-04-26 North West 55
## 706 2020-04-27 North West 54
## 707 2020-04-28 North West 57
## 708 2020-04-29 North West 63
## 709 2020-04-30 North West 59
## 710 2020-05-01 North West 45
## 711 2020-05-02 North West 56
## 712 2020-05-03 North West 55
## 713 2020-05-04 North West 48
## 714 2020-05-05 North West 48
## 715 2020-05-06 North West 44
## 716 2020-05-07 North West 49
## 717 2020-05-08 North West 42
## 718 2020-05-09 North West 31
## 719 2020-05-10 North West 42
## 720 2020-05-11 North West 35
## 721 2020-05-12 North West 38
## 722 2020-05-13 North West 25
## 723 2020-05-14 North West 26
## 724 2020-05-15 North West 33
## 725 2020-05-16 North West 32
## 726 2020-05-17 North West 24
## 727 2020-05-18 North West 31
## 728 2020-05-19 North West 35
## 729 2020-05-20 North West 27
## 730 2020-05-21 North West 27
## 731 2020-05-22 North West 26
## 732 2020-05-23 North West 31
## 733 2020-05-24 North West 26
## 734 2020-05-25 North West 31
## 735 2020-05-26 North West 27
## 736 2020-05-27 North West 27
## 737 2020-05-28 North West 28
## 738 2020-05-29 North West 20
## 739 2020-05-30 North West 19
## 740 2020-05-31 North West 13
## 741 2020-06-01 North West 12
## 742 2020-06-02 North West 27
## 743 2020-06-03 North West 22
## 744 2020-06-04 North West 22
## 745 2020-06-05 North West 16
## 746 2020-06-06 North West 26
## 747 2020-06-07 North West 20
## 748 2020-06-08 North West 23
## 749 2020-06-09 North West 17
## 750 2020-06-10 North West 16
## 751 2020-06-11 North West 16
## 752 2020-06-12 North West 11
## 753 2020-06-13 North West 10
## 754 2020-06-14 North West 15
## 755 2020-06-15 North West 16
## 756 2020-06-16 North West 15
## 757 2020-06-17 North West 13
## 758 2020-06-18 North West 14
## 759 2020-06-19 North West 7
## 760 2020-06-20 North West 11
## 761 2020-06-21 North West 8
## 762 2020-06-22 North West 11
## 763 2020-06-23 North West 13
## 764 2020-06-24 North West 13
## 765 2020-06-25 North West 15
## 766 2020-06-26 North West 6
## 767 2020-06-27 North West 7
## 768 2020-06-28 North West 9
## 769 2020-06-29 North West 9
## 770 2020-06-30 North West 7
## 771 2020-07-01 North West 3
## 772 2020-07-02 North West 6
## 773 2020-07-03 North West 7
## 774 2020-07-04 North West 4
## 775 2020-07-05 North West 6
## 776 2020-07-06 North West 9
## 777 2020-07-07 North West 8
## 778 2020-07-08 North West 5
## 779 2020-07-09 North West 10
## 780 2020-07-10 North West 2
## 781 2020-07-11 North West 5
## 782 2020-07-12 North West 0
## 783 2020-07-13 North West 6
## 784 2020-07-14 North West 4
## 785 2020-07-15 North West 5
## 786 2020-07-16 North West 2
## 787 2020-07-17 North West 4
## 788 2020-07-18 North West 4
## 789 2020-07-19 North West 3
## 790 2020-07-20 North West 0
## 791 2020-07-21 North West 2
## 792 2020-07-22 North West 3
## 793 2020-07-23 North West 2
## 794 2020-07-24 North West 1
## 795 2020-07-25 North West 0
## 796 2020-07-26 North West 3
## 797 2020-07-27 North West 1
## 798 2020-07-28 North West 1
## 799 2020-07-29 North West 1
## 800 2020-07-30 North West 1
## 801 2020-07-31 North West 0
## 802 2020-08-01 North West 2
## 803 2020-08-02 North West 0
## 804 2020-08-03 North West 7
## 805 2020-08-04 North West 3
## 806 2020-08-05 North West 0
## 807 2020-08-06 North West 0
## 808 2020-08-07 North West 0
## 809 2020-08-08 North West 0
## 810 2020-08-09 North West 0
## 811 2020-03-01 South East 0
## 812 2020-03-02 South East 0
## 813 2020-03-03 South East 1
## 814 2020-03-04 South East 0
## 815 2020-03-05 South East 1
## 816 2020-03-06 South East 0
## 817 2020-03-07 South East 0
## 818 2020-03-08 South East 1
## 819 2020-03-09 South East 1
## 820 2020-03-10 South East 1
## 821 2020-03-11 South East 1
## 822 2020-03-12 South East 0
## 823 2020-03-13 South East 1
## 824 2020-03-14 South East 1
## 825 2020-03-15 South East 5
## 826 2020-03-16 South East 8
## 827 2020-03-17 South East 7
## 828 2020-03-18 South East 10
## 829 2020-03-19 South East 9
## 830 2020-03-20 South East 13
## 831 2020-03-21 South East 7
## 832 2020-03-22 South East 25
## 833 2020-03-23 South East 20
## 834 2020-03-24 South East 22
## 835 2020-03-25 South East 29
## 836 2020-03-26 South East 35
## 837 2020-03-27 South East 34
## 838 2020-03-28 South East 36
## 839 2020-03-29 South East 55
## 840 2020-03-30 South East 58
## 841 2020-03-31 South East 65
## 842 2020-04-01 South East 66
## 843 2020-04-02 South East 55
## 844 2020-04-03 South East 72
## 845 2020-04-04 South East 80
## 846 2020-04-05 South East 82
## 847 2020-04-06 South East 88
## 848 2020-04-07 South East 100
## 849 2020-04-08 South East 83
## 850 2020-04-09 South East 104
## 851 2020-04-10 South East 88
## 852 2020-04-11 South East 88
## 853 2020-04-12 South East 88
## 854 2020-04-13 South East 84
## 855 2020-04-14 South East 65
## 856 2020-04-15 South East 72
## 857 2020-04-16 South East 56
## 858 2020-04-17 South East 86
## 859 2020-04-18 South East 57
## 860 2020-04-19 South East 70
## 861 2020-04-20 South East 87
## 862 2020-04-21 South East 51
## 863 2020-04-22 South East 54
## 864 2020-04-23 South East 57
## 865 2020-04-24 South East 64
## 866 2020-04-25 South East 51
## 867 2020-04-26 South East 51
## 868 2020-04-27 South East 41
## 869 2020-04-28 South East 40
## 870 2020-04-29 South East 47
## 871 2020-04-30 South East 29
## 872 2020-05-01 South East 37
## 873 2020-05-02 South East 36
## 874 2020-05-03 South East 17
## 875 2020-05-04 South East 35
## 876 2020-05-05 South East 29
## 877 2020-05-06 South East 25
## 878 2020-05-07 South East 27
## 879 2020-05-08 South East 26
## 880 2020-05-09 South East 28
## 881 2020-05-10 South East 19
## 882 2020-05-11 South East 25
## 883 2020-05-12 South East 27
## 884 2020-05-13 South East 18
## 885 2020-05-14 South East 32
## 886 2020-05-15 South East 25
## 887 2020-05-16 South East 22
## 888 2020-05-17 South East 18
## 889 2020-05-18 South East 22
## 890 2020-05-19 South East 12
## 891 2020-05-20 South East 22
## 892 2020-05-21 South East 15
## 893 2020-05-22 South East 17
## 894 2020-05-23 South East 21
## 895 2020-05-24 South East 17
## 896 2020-05-25 South East 13
## 897 2020-05-26 South East 19
## 898 2020-05-27 South East 19
## 899 2020-05-28 South East 12
## 900 2020-05-29 South East 22
## 901 2020-05-30 South East 8
## 902 2020-05-31 South East 12
## 903 2020-06-01 South East 11
## 904 2020-06-02 South East 13
## 905 2020-06-03 South East 18
## 906 2020-06-04 South East 11
## 907 2020-06-05 South East 11
## 908 2020-06-06 South East 10
## 909 2020-06-07 South East 12
## 910 2020-06-08 South East 8
## 911 2020-06-09 South East 10
## 912 2020-06-10 South East 11
## 913 2020-06-11 South East 5
## 914 2020-06-12 South East 6
## 915 2020-06-13 South East 7
## 916 2020-06-14 South East 7
## 917 2020-06-15 South East 8
## 918 2020-06-16 South East 14
## 919 2020-06-17 South East 9
## 920 2020-06-18 South East 4
## 921 2020-06-19 South East 7
## 922 2020-06-20 South East 5
## 923 2020-06-21 South East 3
## 924 2020-06-22 South East 2
## 925 2020-06-23 South East 8
## 926 2020-06-24 South East 7
## 927 2020-06-25 South East 5
## 928 2020-06-26 South East 8
## 929 2020-06-27 South East 9
## 930 2020-06-28 South East 6
## 931 2020-06-29 South East 5
## 932 2020-06-30 South East 5
## 933 2020-07-01 South East 2
## 934 2020-07-02 South East 8
## 935 2020-07-03 South East 3
## 936 2020-07-04 South East 6
## 937 2020-07-05 South East 4
## 938 2020-07-06 South East 4
## 939 2020-07-07 South East 6
## 940 2020-07-08 South East 3
## 941 2020-07-09 South East 7
## 942 2020-07-10 South East 3
## 943 2020-07-11 South East 4
## 944 2020-07-12 South East 4
## 945 2020-07-13 South East 5
## 946 2020-07-14 South East 5
## 947 2020-07-15 South East 6
## 948 2020-07-16 South East 3
## 949 2020-07-17 South East 1
## 950 2020-07-18 South East 5
## 951 2020-07-19 South East 2
## 952 2020-07-20 South East 6
## 953 2020-07-21 South East 4
## 954 2020-07-22 South East 2
## 955 2020-07-23 South East 3
## 956 2020-07-24 South East 1
## 957 2020-07-25 South East 1
## 958 2020-07-26 South East 3
## 959 2020-07-27 South East 0
## 960 2020-07-28 South East 3
## 961 2020-07-29 South East 2
## 962 2020-07-30 South East 3
## 963 2020-07-31 South East 1
## 964 2020-08-01 South East 2
## 965 2020-08-02 South East 3
## 966 2020-08-03 South East 0
## 967 2020-08-04 South East 0
## 968 2020-08-05 South East 0
## 969 2020-08-06 South East 0
## 970 2020-08-07 South East 0
## 971 2020-08-08 South East 1
## 972 2020-08-09 South East 0
## 973 2020-03-01 South West 0
## 974 2020-03-02 South West 0
## 975 2020-03-03 South West 0
## 976 2020-03-04 South West 0
## 977 2020-03-05 South West 0
## 978 2020-03-06 South West 0
## 979 2020-03-07 South West 0
## 980 2020-03-08 South West 0
## 981 2020-03-09 South West 0
## 982 2020-03-10 South West 0
## 983 2020-03-11 South West 1
## 984 2020-03-12 South West 0
## 985 2020-03-13 South West 0
## 986 2020-03-14 South West 1
## 987 2020-03-15 South West 0
## 988 2020-03-16 South West 0
## 989 2020-03-17 South West 2
## 990 2020-03-18 South West 2
## 991 2020-03-19 South West 4
## 992 2020-03-20 South West 3
## 993 2020-03-21 South West 6
## 994 2020-03-22 South West 7
## 995 2020-03-23 South West 8
## 996 2020-03-24 South West 7
## 997 2020-03-25 South West 9
## 998 2020-03-26 South West 11
## 999 2020-03-27 South West 13
## 1000 2020-03-28 South West 21
## 1001 2020-03-29 South West 18
## 1002 2020-03-30 South West 23
## 1003 2020-03-31 South West 23
## 1004 2020-04-01 South West 21
## 1005 2020-04-02 South West 23
## 1006 2020-04-03 South West 30
## 1007 2020-04-04 South West 42
## 1008 2020-04-05 South West 32
## 1009 2020-04-06 South West 34
## 1010 2020-04-07 South West 39
## 1011 2020-04-08 South West 47
## 1012 2020-04-09 South West 24
## 1013 2020-04-10 South West 46
## 1014 2020-04-11 South West 43
## 1015 2020-04-12 South West 23
## 1016 2020-04-13 South West 27
## 1017 2020-04-14 South West 24
## 1018 2020-04-15 South West 32
## 1019 2020-04-16 South West 29
## 1020 2020-04-17 South West 33
## 1021 2020-04-18 South West 25
## 1022 2020-04-19 South West 31
## 1023 2020-04-20 South West 26
## 1024 2020-04-21 South West 26
## 1025 2020-04-22 South West 23
## 1026 2020-04-23 South West 17
## 1027 2020-04-24 South West 19
## 1028 2020-04-25 South West 15
## 1029 2020-04-26 South West 27
## 1030 2020-04-27 South West 13
## 1031 2020-04-28 South West 17
## 1032 2020-04-29 South West 15
## 1033 2020-04-30 South West 26
## 1034 2020-05-01 South West 6
## 1035 2020-05-02 South West 7
## 1036 2020-05-03 South West 10
## 1037 2020-05-04 South West 17
## 1038 2020-05-05 South West 14
## 1039 2020-05-06 South West 19
## 1040 2020-05-07 South West 16
## 1041 2020-05-08 South West 6
## 1042 2020-05-09 South West 11
## 1043 2020-05-10 South West 5
## 1044 2020-05-11 South West 8
## 1045 2020-05-12 South West 7
## 1046 2020-05-13 South West 7
## 1047 2020-05-14 South West 6
## 1048 2020-05-15 South West 4
## 1049 2020-05-16 South West 4
## 1050 2020-05-17 South West 6
## 1051 2020-05-18 South West 4
## 1052 2020-05-19 South West 6
## 1053 2020-05-20 South West 1
## 1054 2020-05-21 South West 9
## 1055 2020-05-22 South West 7
## 1056 2020-05-23 South West 6
## 1057 2020-05-24 South West 3
## 1058 2020-05-25 South West 8
## 1059 2020-05-26 South West 11
## 1060 2020-05-27 South West 5
## 1061 2020-05-28 South West 10
## 1062 2020-05-29 South West 7
## 1063 2020-05-30 South West 3
## 1064 2020-05-31 South West 2
## 1065 2020-06-01 South West 7
## 1066 2020-06-02 South West 2
## 1067 2020-06-03 South West 7
## 1068 2020-06-04 South West 2
## 1069 2020-06-05 South West 2
## 1070 2020-06-06 South West 1
## 1071 2020-06-07 South West 3
## 1072 2020-06-08 South West 3
## 1073 2020-06-09 South West 0
## 1074 2020-06-10 South West 1
## 1075 2020-06-11 South West 2
## 1076 2020-06-12 South West 2
## 1077 2020-06-13 South West 2
## 1078 2020-06-14 South West 0
## 1079 2020-06-15 South West 2
## 1080 2020-06-16 South West 2
## 1081 2020-06-17 South West 0
## 1082 2020-06-18 South West 0
## 1083 2020-06-19 South West 0
## 1084 2020-06-20 South West 2
## 1085 2020-06-21 South West 0
## 1086 2020-06-22 South West 1
## 1087 2020-06-23 South West 1
## 1088 2020-06-24 South West 1
## 1089 2020-06-25 South West 0
## 1090 2020-06-26 South West 3
## 1091 2020-06-27 South West 0
## 1092 2020-06-28 South West 0
## 1093 2020-06-29 South West 1
## 1094 2020-06-30 South West 0
## 1095 2020-07-01 South West 0
## 1096 2020-07-02 South West 0
## 1097 2020-07-03 South West 0
## 1098 2020-07-04 South West 0
## 1099 2020-07-05 South West 1
## 1100 2020-07-06 South West 0
## 1101 2020-07-07 South West 0
## 1102 2020-07-08 South West 2
## 1103 2020-07-09 South West 0
## 1104 2020-07-10 South West 1
## 1105 2020-07-11 South West 0
## 1106 2020-07-12 South West 0
## 1107 2020-07-13 South West 1
## 1108 2020-07-14 South West 0
## 1109 2020-07-15 South West 0
## 1110 2020-07-16 South West 0
## 1111 2020-07-17 South West 1
## 1112 2020-07-18 South West 0
## 1113 2020-07-19 South West 0
## 1114 2020-07-20 South West 0
## 1115 2020-07-21 South West 0
## 1116 2020-07-22 South West 0
## 1117 2020-07-23 South West 0
## 1118 2020-07-24 South West 0
## 1119 2020-07-25 South West 0
## 1120 2020-07-26 South West 0
## 1121 2020-07-27 South West 0
## 1122 2020-07-28 South West 0
## 1123 2020-07-29 South West 0
## 1124 2020-07-30 South West 1
## 1125 2020-07-31 South West 0
## 1126 2020-08-01 South West 0
## 1127 2020-08-02 South West 0
## 1128 2020-08-03 South West 0
## 1129 2020-08-04 South West 0
## 1130 2020-08-05 South West 0
## 1131 2020-08-06 South West 0
## 1132 2020-08-07 South West 0
## 1133 2020-08-08 South West 0
## 1134 2020-08-09 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Monday 10 Aug 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.362 -6.027 -1.448 4.302 10.669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.327e+00 7.457e-02 58.02 <2e-16 ***
## note_lag 1.676e-05 7.787e-07 21.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 36.46705)
##
## Null deviance: 18828.5 on 100 degrees of freedom
## Residual deviance: 3911.6 on 99 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 75.690175 1.000017
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 65.210724 87.361517
## note_lag 1.000015 1.000018
Rsq(lag_mod)
## [1] 0.792252
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64"
## nodename
## "Mac-1597139943921.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.2 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.15
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.1 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.1 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.5.0 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-9 zip_2.1.0 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.13.0 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.16
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1